Tag Archives: portfolio optimization

I expect that next week’s reinsurance jamboree in Monte Carlo will be full of talk of innovative and technology streaming-lining business models (as per this post on AI and insurance). This recent article from the FT is just one example of claims that technology like blockchain can reduce costs by 30%. The article highlights questions about whether insurers are prepared to give up ownership of data, arguably their competitive advantage, if the technology is really to be scaled up in the sector.

As a reminder of the reinsurance sector’s cost issues, as per this post on Lloyds’, the graph below illustrates the trend across Lloyds’, the Aon Benfield Aggregate portfolio, and Munich’s P&C reinsurance business.

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Until the sector gets serious about cutting costs, such as overpaid executives on luxury islands or expensive cities and antiquated business practises such as holding get togethers in places like Monte Carlo, I suspect expenses will remain an issue. In their July review, Willis stated that a “number of traditional carriers are well advanced in their plans to reduce their costs, including difficult decisions around headcount” and that “in addition to cost savings, the more proactively managed carriers are applying far greater rigor in examining the profitability of every line of business they are accepting”. Willis highlighted the potential difficulties for the vastly inefficient MGA business that many have been so actively pursuing. As an example of the type of guff executives will trot out next week, Swiss Re CEO, Christian Mumenthaler, said “we remain convinced that technology will fundamentally change the re/insurance value chain”, likely speaking from some flash office block in one of the most expensive cities in the world!

On market conditions, there was positive developments on reinsurance pricing at the January renewals after the 2017 losses with underlying insurance rates improving, as illustrated by the Marsh composite commercial rate index (example from US below).

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However, commentators have been getting ever more pessimistic as the year progresses, particularly after the mid-year renewals. Deutsche Bank recently called the reinsurance pricing outlook “very bleak”. A.M. Best stated that “the new normal for reinsurers appears to be one with returns that are less impressive and underwriting and fee income becoming a larger contributor to profits” and predicts, assuming a normal large loss level, an 8% ROE for 2018 for the sector. Willis, in their H1 report, puts the sectors ROE at 7.7% for H1 2018. S&P, in the latest report that is part of their Global Highlights series, also expects a ROE return for 2018 around 6% to 8% and estimates that “reinsurers are likely to barely cover their cost of capital in 2018 and 2019”.

S&P does question why “the market values the industry at a premium to book value today (on average at 1.24x at year-end 2017), and at near historical highs, given the challenges” and believes that potential capital returns, M&A and interest rate rises are all behind elevated valuations. The recent Apollo PE deal for Aspen at 1.12 times book seems a large way off other recent multiples, as per this post, but Aspen has had performance issues. Still its interesting that no other insurer was tempted to have a go at Aspen with the obvious synergies that such a deal could have achieved. There is only a relatively small number of high quality players left for the M&A game and they will not be cheap!

As you are likely aware, I have been vocal on the impact the ILS sector has had in recent years (most recently here and here). With so-called alternative capital (at what size does it stop being alternative!) now at the $95 billion-mark according to Aon, A.M. Best makes the obvious point that “any hope for near-term improvement in the market is directly correlated to the current level of excess capacity in the overall market today, which is being compounded by the continued inflow of alternative capacity”. Insurers and reinsurers are not only increasing their usage of ILS in portfolio optimisation but are also heavily participating in the sector. The recent purchase by Markel of the industry leading and oldest ILS fund Nephila is an interesting development as Markel already had an ILS platform and is generally not prone to overpaying.

I did find this comment from Bob Swarup of Camdor in a recent Clear Path report on ILS particularly telling – “As an asset class matures it inevitably creates its own cycle and beta. At this point you expect fees to decline both as a function of the benefits of scale but also as it becomes more understood, less of it becomes alpha and more of it becomes beta” and “I do feel that the fees are most definitely too high right now and to a large extent this is because people are trying to treat this as an alternative asset class whereas it is large enough now to be part of the general mix”. Given the still relatively small size of the ILS sector, it’s difficult for ILS managers to demonstrate true alpha at scale (unless they are taking crazy leveraged bets!) and therefore pressure on current fees will become a feature.

A.M. Best articulated my views on ILS succinctly as follows: “The uncorrelated nature of the industry to traditional investments does appear to have value—so long as the overall risk-adjusted return remains appropriate”. The graph below from artemis.bm shows the latest differential between returns and expected cost across the portfolio they monitor.

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In terms of the returns from ILS funds, the graph below shows the underlying trend (with 2018 results assuming no abnormal catastrophic activity) of insurance only returns from indices calculated by Lane Financial (here) and Eurekahedge (here). Are recent 5 year average returns of between 500 and 250 basis points excess risk free enough to compensation for the risk of a relatively concentrated portfolio? Some think so. I don’t.

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Whether reinsurers and specialty insurers will be able to maintain superior (albeit just above CoC) recent returns over ILS, as illustrated in this post, through arbitrating lower return ILS capital or whether their bloated costs structures will catch them out will be a fascinating game to watch over the coming years. I found a section of a recent S&P report, part of their Global Highlights series, on cat exposures in the sector, amusing. It stated that in 2017 “the reinsurance industry recorded an aggregate loss that was assessed as likely to be incurred less than once in 20 years” whilst “this was the third time this had happened in less than 20 years“.

So, all in all, the story is depressingly familiar for the sector. The new normal, as so many commentators have recently called it, amounts to overcapacity, weak pricing power, bloated cost structures, and optimistic valuations. Let’s see if anybody has anything new or interesting to say in Monte Carlo next week.

As always, let’s hope there is minimal human damage from any hurricanes such as the developing Hurricane Florence or other catastrophic events in 2018.

It seems like wherever you look these days there is references to the transformational power of artificial intelligence (AI), including cognitive or machine learning (ML), on businesses and our future. A previous post on AI and insurance referred to some of the changes ongoing in financial services in relation to core business processes and costs. This industry article highlights how machine learning (specifically multi-objective genetic algorithms) can be used in portfolio optimization by (re)insurers. To further my understanding on the topic, I recently bought a copy of a new book called “Advances in Financial Machine Learning” by Marcos Lopez de Prado, although I suspect I will be out of my depth on the technical elements of the book. Other posts on this blog (such as this one) on the telecom sector refer to the impact intelligent networks are having on telecom business models. One example is the efficiencies Centurylink (CTL) have shown in their capital expenditure allocation processes from using AI and this got me thinking about the competitive impact such technology will have on costs across numerous traditional industries.

AI is a complex topic and in its broadest context it covers computer systems that can sense their environment, think, and in some cases learn, and take applicable actions according to their objectives. To illustrate the complexity of the topic, neural networks are a subset of machine learning techniques. Essentially, they are AI systems based on simulating connected “neural units” loosely modelling the way that neurons interact in the brain. Neural networks need large data sets to be “trained” and the number of layers of simulated interconnected neurons, often numbering in their millions, determine how “deep” the learning can be. Before I embarrass myself in demonstrating how little I know about the technicalities of this topic, it’s safe to say AI as referred to in this post encompasses the broadest definition, unless a referenced report or article specifically narrows the definition to a subset of the broader definition and is highlighted as such.

According to IDC (here), “interest and awareness of AI is at a fever pitch” and global spending on AI systems is projected to grow from approximately $20 billion this year to $50 billion in 2021. David Schubmehl of IDC stated that “by 2019, 40% of digital transformation initiatives will use AI services and by 2021, 75% of enterprise applications will use AI”. By the end of this year, retail will be the largest spender on AI, followed by banking, discrete manufacturing, and healthcare. Retail AI use cases include automated customer service agents, expert shopping advisors and product recommendations, and merchandising for omni channel operations. Banking AI use cases include automated threat intelligence and prevention systems, fraud analysis and investigation, and program advisors and recommendation systems. Discrete manufacturing AI use cases including automated preventative maintenance, quality management investigation and recommendation systems. Improved diagnosis and treatment systems are a key focus in healthcare.

In this April 2018 report, McKinsey highlights numerous use cases concluding that ”AI can most often be adopted and create value where other analytics methods and techniques are also creating value”. McKinsey emphasis that “abundant volumes of rich data from images, audio, and video, and large-scale text are the essential starting point and lifeblood of creating value with AI”. McKinsey’s AI focus in the report is particularly in relation to deep learning techniques such as feed forward neural networks, recurrent neural networks, and convolutional neural networks.

Examples highlighted by McKinsey include a European trucking company who reduced fuel costs by 15 percent by using AI to optimize routing of delivery traffic, an airline who uses AI to predict congestion and weather-related problems to avoid costly cancellations, and a travel company who increase ancillary revenue by 10-15% using a recommender system algorithm trained on product and customer data to offer additional services. Other specific areas highlighted by McKinsey are captured in the following paragraph:

“AI’s ability to conduct preventive maintenance and field force scheduling, as well as optimizing production and assembly processes, means that it also has considerable application possibilities and value potential across sectors including advanced electronics and semiconductors, automotive and assembly, chemicals, basic materials, transportation and logistics, oil and gas, pharmaceuticals and medical products, aerospace and defense, agriculture, and consumer packaged goods. In advanced electronics and semiconductors, for example, harnessing data to adjust production and supply-chain operations can minimize spending on utilities and raw materials, cutting overall production costs by 5 to 10 percent in our use cases.”

McKinsey calculated the value potential of AI from neural networks across numerous sectors, as per the graph below, amounting to $3.5 to $5.8 trillion. Value potential is defined as both in the form of increased profits for companies and lower prices or higher quality products and services captured by customers, based off the 2016 global economy. They did not estimate the value potential of creating entirely new product or service categories, such as autonomous driving.

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McKinsey identified several challenges and limitations with applying AI techniques, as follows:

Making an effective use of neural networks requires labelled training data sets and therefore data quality is a key issue. Ironically, machine learning often requires large amounts of manual effort in “teaching” machines to learn. The experience of Microsoft with their chatter bot Tay in 2016 illustrates the shortcoming of learning from bad data!

Obtaining data sets that are sufficiently large and comprehensive to be used for comprehensive training is also an issue. According to the authors of the book “Deep Learning”, a supervised deep-learning algorithm will generally achieve acceptable performance with around 5,000 labelled examples per category and will match or exceed human level performance when trained with a data set containing at least 10 million labelled examples.

Explaining the results from large and complex models in terms of existing practices and regulatory frameworks is another issue. Product certifications in health care, automotive, chemicals, aerospace industries and regulations in the financial services sector can be an obstacle if processes and outcomes are not clearly explainable and auditable. Some nascent approaches to increasing model transparency, including local-interpretable-model-agnostic explanations (LIME), may help resolve this explanation challenge.

AI models continue to have difficulties in carrying their experiences from one set of circumstances to another, applying a generalisation to learning. That means companies must commit resources to train new models for similar use cases. Transfer learning, in which an AI model is trained to accomplish a certain task and then quickly applies that learning to a similar but distinct activity, is one area of focus in response to this issue.

Finally, one area that has been the subject of focus is the risk of bias in data and algorithms. As bias is part of the human condition, it is engrained in our behaviour and historical data. This article in the New Scientist highlights five examples.

In 2016, Accenture estimated that US GDP could be $8.3 trillion higher in 2035 because of AI, doubling growth rates largely due to AI induced productivity gains. More recently in February this year, PwC published a report on an extensive macro-economic impact of AI and projected a baseline scenario that global GDP will be 14% higher due to AI, with the US and China benefiting the most. Using a Spatial Computable General Equilibrium Model (SCGE) of the global economy, PwC quantifies the total economic impact (as measured by GDP) of AI on the global economy via both productivity gains and consumption-side product enhancements over the period 2017-2030. The impact on the seven regions modelled by 2030 can be seen below.

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PwC estimates that the economic impact of AI will be driven by productivity gains from businesses automating processes as well as augmenting their existing labour force with AI technologies (assisted, autonomous and augmented intelligence) and by increased consumer demand resulting from the availability of personalised and/or higher-quality AI-enhanced products and services.

In terms of sectors, PwC estimate the services industry that encompasses health, education, public services and recreation stands to gain the most, with retail and wholesale trade as well as accommodation and food services also expected to see a large boost. Transport and logistics as well as financial and professional services will also see significant but smaller GDP gains by 2030 because of AI although they estimate that the financial service sector gains relatively quickly in the short term. Unsurprisingly, PwC finds that capital intensive industries have the greatest productivity gains from AI uptake and specifically highlight the Technology, Media and Telecommunications (TMT) sector as having substantial marginal productivity gains from uptaking replacement and augmenting AI. The sectoral gains estimated by PwC by 2030 are shown below.

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A key element of these new processes is the computing capabilities needed to process so much data that underlies AI. This recent article in the FT highlighted how the postulated demise of Moore’s law after its 50-year run is impacting the micro-chip sector. Mike Mayberry of Intel commented that “the future is more heterogeneous” when referring to the need for the chip industry to optimise chip design for specific tasks. DARPA, the US defence department’s research arm, has allocated $1.5 billion in research grants on the chips of the future, such as chip architectures that combine both power and flexibility using reprogrammable “software-defined hardware”. This increase in focus from the US is a direct counter against China’s plans to develop its intellectual and technical abilities in semiconductors over the coming years beyond simple manufacturing.

One of the current leaders in specialised chip design is Nvidia (NVDA) who developed software lead chips for video cards in the gaming sector through their graphics processing unit (GPU). The GPU accelerates applications running on standard central processing units (CPU) by offloading some of the compute-intensive and time-consuming portions of the code whilst the rest of the application still runs on the CPU. The chips developed by NVDA for gamers have proven ideal in handling the huge volumes of data needed to train deep learning systems that are used in AI. The exhibit below from NVDA illustrates how they assert that new processes such as GPU can overcome the slowdown in capability from the density limitation of Moore’s Law.

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NVDA, whose stock is up over 400% in the past 24 months, has been a darling of the stock market in recent years and reported strong financial figures for their quarter to end April, as shown below. Their quarterly figures to the end of July are eagerly expected next month. NVDA has been range bound in recent months, with the trade war often cited as a concern with their products sold approximately 20%, 20%, and 30% into supply chains in China, other Asia Pacific countries, and Taiwan respectively

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Although seen as the current leader, NVDA is not alone in this space. AMD recently reported strong Q1 2018 results, with revenues up 40%, and has a range of specialised chip designs to compete in the datacentre, auto, and machine learning sectors. AMD’s improved results also reduce risk on their balance sheet with leverage decreasing from 4.6X to 3.4X and projected to decline further. AMD’s stock is up approximately 70% year to date. AMD’s 7-nanonmeter product launch planned for later this year also compares favourably against Intel’s delayed release date to 2019 for its 10-nanometer chips.

Intel has historically rolled out a new generation of computer chips every two years, enabling chips that were consistently more powerful than their predecessors even as the cost of that computing power fell. But as Intel has run up against the limits of physics, they have reverted to making upgrades to its aging 14nm processor node, which they say performs 70% better than when initially released four years ago. Despite advances by NVDA and AMD in data centres, Intel chips still dominate. In relation to the AI market, Intel is focused on an approach called field-programmable gate array (FPGA) which is an integrated circuit designed to be configured by a customer or a designer after manufacturing. This approach of domain-specific architectures is seen as an important trend in the sector for the future.

Another interesting development is Google (GOOG) recently reported move to commercially sell, through its cloud-computing service, its own big-data chip design that it has been using internally for some time. Known as a tensor processing unit (TPU), the chip was specifically developed by GOOG for neural network machine learning and is an AI accelerator application-specific integrated circuit (ASIC). For example, in Google photos an individual TPU can process over 100 million photos a day. What GOOG will do with this technology will be an interesting development to watch.

Given the need for access to large labelled data sets and significant computing infrastructure, the large internet firms like Google, Facebook (FB), Microsoft (MSFT), Amazon (AMZN) and Chinese firms like Baidu (BIDU) and Tencent (TCEHY) are natural leaders in using and commercialising AI. Other firms highlighted by analysts as riding the AI wave include Xilinx (XLNX), a developer of high-performance FPGAs, and Yext (YEXT), who specialise in managing digital information relevant to specific brands, and Twilio (TWLO), a specialist invoice and text communication analysis. YEXT and TWLO are loss making. All of these stocks, possibly excluding the Chinese ones, are trading at lofty valuations. If the current wobbles on the stock market do lead to a significant fall in technology valuations, the stocks on my watchlist will be NVDA, BIDU and GOOG. I’d ignore the one trick ponys, particularly the loss making ones! Specifically, Google is one I have been trying to get in for years at a sensible value and I will watch NVDA’s results next month with keen interest as they have consistently broken estimates in recent quarters. Now, if only the market would fall from its current heights to allow for a sensible entry point…….maybe enabled by algorithmic trading or a massive trend move by the passives!

Disclaimer

This blog represents my personal views and is not reflective of the views or opinions held by any company or employer I work for currently or have worked for in the past. The views expressed herein are based solely upon publicly available data. No views expressed herein should be taken as an endorsement to take any particular course of action in the markets. The basis of this blog is that different views should be expressed and readers make up their own minds on the what they believe and act accordingly.